code: def super_lm_base():
      """Set of hyperparameters."""
      hparams = common_hparams.basic_params1()
      hparams.hidden_size = 512
      hparams.moe_hidden_sizes = "512"
      hparams.batch_size = 16384
      hparams.max_length = 0
      # All hyperparameters ending in "dropout" are automatically set to 0.0
      # when not in training mode.
      hparams.layer_prepostprocess_dropout = 0.0
      hparams.symbol_dropout = 0.1
      hparams.add_hparam("attention_dropout", 0.0)
      hparams.label_smoothing = 0.0
      hparams.clip_grad_norm = 0.0  # i.e. no gradient clipping
      hparams.optimizer = "Adafactor"
      hparams.learning_rate_decay_scheme = "noam"
      hparams.learning_rate = 0.1
      hparams.learning_rate_warmup_steps = 8000
      hparams.initializer_gain = 1.0
      hparams.initializer = "uniform_unit_scaling"
      hparams.weight_decay = 0.0
      hparams.shared_embedding_and_softmax_weights = False
      hparams.layer_preprocess_sequence = "n"
      hparams.layer_postprocess_sequence = "da"
      # we only want one data shard.
      hparams.no_data_parallelism = True
      # bypass the symbol modality so that we can use model parallelism.
      hparams.bottom = {
          "inputs": modalities.identity_bottom,
          "targets": modalities.identity_bottom,
      }
      hparams.top = {
          "targets": modalities.identity_top,
      }
      hparams.add_hparam("filter_size", 512)
      hparams.add_hparam("mix_fraction", 0.5)
      # attention-related flags
      hparams.add_hparam("multihead_attention_num_heads", 4)
      hparams.add_hparam("multihead_attention_key_channels", 0)
      hparams.add_hparam("multihead_attention_value_channels", 0)
      hparams.add_hparam("pos", "timing")  # timing, none
      hparams.add_hparam(
          "layers", ("n,att,m,d,a," "n,ffn,m,d,a,") * 4 + "n,ffn,d"
      )
      # Number of model shards - each one has separate parameters.
      # Changing this number invalidates checkpoints.
      hparams.add_hparam("num_model_shards", 8)
      hparams.add_hparam("diet_experts", False)
      return hparams
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from six.moves import range
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import modalities
  from tensor2tensor.utils import diet
  from tensor2tensor.utils import expert_utils
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 2 for calling a global function with example usage: super_lm_base() and returns: hparams